A recommendation model for personalized book lists

In this study, we present a novel approach to recommend the personalized book lists for the university members. Our approach consists of clustering the university members into different clusters based on their recent circulation activities and discovering the interest patterns of members in the cluster. In the first step, we clustered members sharing the common interests to the same cluster by using K-means algorithm, after that we explored the possible interest patterns performed by members in each cluster by association rules. Finally, we provided the recommended book lists that satisfy their individual needs and interest patterns. A questionnaire survey was performed to evaluate the accuracy satisfaction of predicting the satisfy book list to an individual. The evaluation results reveal the possibility of using circulation activity history to predict the current interest of an individual member and construct the personalized book lists that satisfy their interests.